Fellow time-nuts,
I keep poking around various processing algorithms trying to figure out
what they do and perform. One aspect which may be interesting to know
about is the use of zero dead time phase or frequency data and the
frequency estimation from that data. One may be compelled to
differentiate the time data into frequency data by using nearby data
samples, according to y(i) = (x(i+1)-x(i))/tau0 and then just form the
average of those. The interesting thing about that calculations is that
the x(i+1) and x(i) terms cancels except for x(1) and x(N) so
effectively only two samples of phase data is being used. This is a
simple illustration of how algorithms may provide less degrees of
freedom than one may initially assume it to have (N-1 in this case).
Similar type of cancellation occurs in linear drift estimation.
Maybe this could spark some interest in the way one estimates the
various parameters and what different estimators may do to cancel noise
of individual samples.
Cheers,
Magnus